Effectiveness of Hybrid AI and Human Suicide Detection Within Digital Peer Support
Siddharth Shukla, Prachet Balaji, Ilayda Ozsan McMillan, Marvyn R. Arévalo Avalos, Harpreet Nagra, Zara Dana

TL;DR
This study shows that combining AI with human support in digital peer networks can effectively detect and help users with suicidal thoughts while improving mental well-being.
Contribution
A hybrid AI-human system for real-time suicide detection in digital peer support is shown to be effective and scalable.
Findings
AI detected suicidal ideation faster than humans in 77.52% of passive and 81.26% of active cases.
Users experienced significant reductions in depression, loneliness, and helplessness, with increased optimism.
5472 active SI users were referred to crisis care within 71.3 seconds of AI alerts.
Abstract
Background: Suicidality continues to rise, while mental health services face obstacles of access, availability, and affordability. Digital peer support (DPS) may help bridge these gaps and facilitate early identification of suicidal ideation (SI). Objective: This study examined (1) the effectiveness of a hybrid solution combining a proprietary AI-based SI detection with real-time human moderation within DPS, (2) distribution of SI, (3) active SI referral, (4) linguistic differences in SI, (5) sentiment changes among users, and (6) the effects of peer SI disclosure. Methods: We retrospectively analyzed 169,181 live-chat transcripts encompassing 449,946 user visits (January–December 2024) from a DPS provider, Supportiv. Passive and active SI were identified using a hybrid AI and human moderator solution with post hoc LLM verification. Sentiment analysis and ANCOVA compared changes in…
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Taxonomy
TopicsDigital Mental Health Interventions · Suicide and Self-Harm Studies · Mental Health via Writing
